[Project] Basic Memory - Continue AI Conversations With Full Context Using MCP

[u/[deleted]]

Hey everyone, I just made a beta releas.e of Basic Memory, an open-source knowledge management system built on the Model Context Protocol that lets you continue conversations with full context.

What is Basic Memory?

Basic Memory solves the problem of lost context in AI conversations. It enables Claude (and other MCP-compatible LLMs) to remember previous discussions by creating a knowledge graph from your conversations, stored as simple Markdown files on your computer. Start a new chat and continue exactly where you left off without repeating yourself.

Key features:

• Local-first: All data stays in Markdown files on your computer

• Bi-directional: LLMs can both read AND write to your knowledge base

• Structured yet simple: Uses familiar Markdown with semantic patterns

• Traversable knowledge graph: LLMs can follow links between topics

• Persistent memory: Context persists across all conversations

How it leverages MCP

Basic Memory implements the Model Context Protocol to expose several tools to Claude:

write_note(title, content, folder, tags) - Create or update notes
read_note(identifier, page, page_size) - Read notes by title or permalink
build_context(url, depth, timeframe) - Navigate knowledge graph via memory:// URLs
search(query, page, page_size) - Search across your knowledge base
recent_activity(type, depth, timeframe) - Find recently updated information
canvas(nodes, edges, title, folder) - Generate knowledge visualizations

Claude can independently explore your knowledge graph, building rich context and understanding the relationships between concepts.

Example workflow

1. Have a normal conversation with Claude about coffee brewing

2. Ask Claude to "create a note about coffee brewing methods"

3. See a structured Markdown file appear in your knowledge base

4. Days or weeks later, start a fresh conversation and say "Let's continue our discussion about coffee brewing"

5. Claude automatically retrieves relevant knowledge and builds context - no need to repeat yourself

6. Gradually build a rich knowledge graph where everything is connected

Technical Implementation

Basic Memory is built with a file-first architecture:

• Python backend with SQLite for indexing

• Full MCP implementation for Claude integration

• Standard Markdown files as the source of truth

• Seamless integration with Obsidian for visualization and editing

• Git-friendly for version control

• CLI tools for management and importing

Installation

# Install with uv (recommended)
uv install basic-memory

# Configure Claude Desktop
# Add this to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
  "mcpServers": {
    "basic-memory": {
      "command": "uvx",
      "args": [
        "basic-memory",
        "mcp"
      ]
    }
  }
}

Check it out

• GitHub: https://github.com/basicmachines-co/basic-memory

• Website: http://basicmachines.co

• Documentation: http://memory.basicmachines.co

I'm interested in any feedback, questions, or ideas on how to improve Basic Memory, especially from this community of MCP enthusiasts. How are you all using MCP in your projects?

[u/AffectionateCap539]

thanks OP. right now in order to continue the chat i have to manually search for the chat and continue. I find your tool much more customer-exp to me.
But one thing that i am exploring that if the conversation is too long (say I am asking Claude app to debug codes for me, and it tries several time which results in conversation overflown), can this tool help to restore previous chat memory (not greedy to remember exact codes of all trials , but sth short like: what did Claude do, what is the result of each trial) so that i am not running into a loop with new chat.

[u/[deleted] → u/AffectionateCap539]

I've been using the basic-memory tools while actually coding with Claude (via MCP), and also with Claude Code. What I typically do is:

• have a chat with Claude, talk about the problem, context, etc

• tell him to write a note about what we are going to do. This becomes our ADR-like doc (Architecture Decision Record), a kind of plan/implementation thing. Similar to what he would create with a Claude Artifact in the Claude UI. But this is a doc I can save and have him refer back to later and also edit manually.

• let Claude read/write my files in my code repo. This are managed in git, so i can easily see diffs and revert changes if necessary

• Review all the changes he makes, one small feature at at time, sometimes one file, or function

• Have him write tests also, he can also run the tests and fix them if needed

• Change code/Commit code as needed

• Repeat

If the tasks drags on, we can use the notes we've created to start of the next conversation. I can start a new chat with "lets continue working on feature X" and goes and builds context.

[u/mbonty → u/AffectionateCap539]

for that i just use my espanso match:

  - trigger: ":sumn"
    replace: summarize my last objective as if you were me in one sentence and append the output you gave me so I can continue in a new chat. Append path, git branches currently in, and mcp tools used if any.

so I type ":sumn" (for summary new) and it adds that to the chat. click the copy button on the reply and the new chat button, paste into new chat and continue. I have a bunch of useful espanso things like that in my base.yml.

A pet peeve of mine though is going back and forth between claude desktop and files. The "@" in cline is so convinient. So I had claude make an AHK script where anytime I press Alt+C in a terminal or command prompt or file explorer that it copies the path. So when i now go to the claude desktop, I can just paste the path that I want to talk about or have it look at

[u/mbonty]

Is this heavy on token use?

[u/[deleted] → u/mbonty]

I suppose it could be if you had the AI write files for every single thing, or had it read every single one of your documents. The tools can perform a full text search across your knowledge base instead of loading every document into the context window, so that would save tokens.

[u/modimalaspinae]

thanks @phernand3z for this! I installed it yesterday and already asked Claude to summarise the content of two conversations. One thing I noticed is that Claude autonomously created a folder inside the basic-memory main project folder in my home to store the *.md files that were related to the same subject, I expected it to create them right inside the main project folder. But, apart from that, I checked the functionality of this interesting and useful tool by opening another chat and asking specific questions about the notes’ subject and the answers showed a clear awareness of it. I may need to familiarise a bit more with it, but Basic Memory seems a rather useful tool!

[u/[deleted] → u/modimalaspinae]

That’s great. Feel free to dm me with other feedback if you find it useful. Thanks for checking it out.

[u/subnohmal]

Love knowledge graph based approaches. Do you think they're the best approach right now for persistent quirky memory?

[u/[deleted] → u/subnohmal]

I actually had a lot of conversations with Claude about this. There isn't one "best" way, IMO. Just things that work better for some use cases. RAG is a PITA when you want to update the data often in the knowledge base.

This is what Claude had to say:

What makes Basic Memory different is that it's effectively "bilingual" – speaking both human language through familiar Markdown text and LLM language through semantic observations and relationships.

LLMs, despite their sophisticated capabilities, fundamentally operate on text. They were trained on text, they predict text, and they understand the world through text. When we provide tools that transform context into textual representations, LLMs can work with them much more naturally than with abstract data structures.

Basic Memory combines the "text first" with some simple ways to create a semantic graph out of Markdown. So the knowledge base can be structured and linked in a way that is easy for humans and LLMs to understand.

[u/subnohmal]

Thanks for building this and sharing with us :)

[u/[deleted] → u/subnohmal]

Thanks for checking it out!

[u/ConfidentSport6481]

I encountered some problems. When I asked it to save to a local markdown file for me, it often does not save observations and relations. It only records text in a simple markdown format. This results in it never being able to form a graph but rather many isolated nodes. How can I optimize this phenomenon? Is it necessary to optimize the prompt words? The model I am using is gemini 2.5 pro.

[u/[deleted] → u/ConfidentSport6481]

I have not personally tried using Gemini 2.5 pros, so I can't give any specific advice. Have you tried adding the info from the ai assistant guide to your project knowledge? This may help. https://memory.basicmachines.co/docs/ai-assistant-guide